{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import warnings\n",
    "\n",
    "if not sys.warnoptions:\n",
    "    warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "from tqdm import tqdm\n",
    "sns.set()\n",
    "tf.compat.v1.random.set_random_seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-11-02</td>\n",
       "      <td>778.200012</td>\n",
       "      <td>781.650024</td>\n",
       "      <td>763.450012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>768.700012</td>\n",
       "      <td>1872400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-03</td>\n",
       "      <td>767.250000</td>\n",
       "      <td>769.950012</td>\n",
       "      <td>759.030029</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>762.130005</td>\n",
       "      <td>1943200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-11-04</td>\n",
       "      <td>750.659973</td>\n",
       "      <td>770.359985</td>\n",
       "      <td>750.560974</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>762.020020</td>\n",
       "      <td>2134800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>774.500000</td>\n",
       "      <td>785.190002</td>\n",
       "      <td>772.549988</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>782.520020</td>\n",
       "      <td>1585100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-11-08</td>\n",
       "      <td>783.400024</td>\n",
       "      <td>795.632996</td>\n",
       "      <td>780.190002</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>790.510010</td>\n",
       "      <td>1350800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2016-11-02  778.200012  781.650024  763.450012  768.700012  768.700012   \n",
       "1  2016-11-03  767.250000  769.950012  759.030029  762.130005  762.130005   \n",
       "2  2016-11-04  750.659973  770.359985  750.560974  762.020020  762.020020   \n",
       "3  2016-11-07  774.500000  785.190002  772.549988  782.520020  782.520020   \n",
       "4  2016-11-08  783.400024  795.632996  780.190002  790.510010  790.510010   \n",
       "\n",
       "    Volume  \n",
       "0  1872400  \n",
       "1  1943200  \n",
       "2  2134800  \n",
       "3  1585100  \n",
       "4  1350800  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../dataset/GOOG-year.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "      <th>0</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.112708</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.090008</td>\n",
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       "      <th>3</th>\n",
       "      <td>0.160459</td>\n",
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      "text/plain": [
       "          0\n",
       "0  0.112708\n",
       "1  0.090008\n",
       "2  0.089628\n",
       "3  0.160459\n",
       "4  0.188066"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "minmax = MinMaxScaler().fit(df.iloc[:, 4:5].astype('float32')) # Close index\n",
    "df_log = minmax.transform(df.iloc[:, 4:5].astype('float32')) # Close index\n",
    "df_log = pd.DataFrame(df_log)\n",
    "df_log.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split train and test\n",
    "\n",
    "I will cut the dataset to train and test datasets,\n",
    "\n",
    "1. Train dataset derived from starting timestamp until last 30 days\n",
    "2. Test dataset derived from last 30 days until end of the dataset\n",
    "\n",
    "So we will let the model do forecasting based on last 30 days, and we will going to repeat the experiment for 10 times. You can increase it locally if you want, and tuning parameters will help you by a lot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((252, 7), (222, 1), (30, 1))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_size = 30\n",
    "simulation_size = 10\n",
    "\n",
    "df_train = df_log.iloc[:-test_size]\n",
    "df_test = df_log.iloc[-test_size:]\n",
    "df.shape, df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Model:\n",
    "    def __init__(\n",
    "        self,\n",
    "        learning_rate,\n",
    "        num_layers,\n",
    "        size,\n",
    "        size_layer,\n",
    "        output_size,\n",
    "        forget_bias = 0.1,\n",
    "    ):\n",
    "        def lstm_cell(size_layer):\n",
    "            return tf.nn.rnn_cell.BasicRNNCell(size_layer)\n",
    "        \n",
    "        with tf.variable_scope('forward', reuse = False):\n",
    "            rnn_cells_forward = tf.nn.rnn_cell.MultiRNNCell(\n",
    "                [lstm_cell(size_layer) for _ in range(num_layers)],\n",
    "                state_is_tuple = False,\n",
    "            )\n",
    "            self.X_forward = tf.placeholder(tf.float32, (None, None, size))\n",
    "            drop_forward = tf.contrib.rnn.DropoutWrapper(\n",
    "                rnn_cells_forward, output_keep_prob = forget_bias\n",
    "            )\n",
    "            self.hidden_layer_forward = tf.placeholder(\n",
    "                tf.float32, (None, num_layers * size_layer)\n",
    "            )\n",
    "            self.outputs_forward, self.last_state_forward = tf.nn.dynamic_rnn(\n",
    "                drop_forward,\n",
    "                self.X_forward,\n",
    "                initial_state = self.hidden_layer_forward,\n",
    "                dtype = tf.float32,\n",
    "            )\n",
    "\n",
    "        with tf.variable_scope('backward', reuse = False):\n",
    "            rnn_cells_backward = tf.nn.rnn_cell.MultiRNNCell(\n",
    "                [lstm_cell(size_layer) for _ in range(num_layers)],\n",
    "                state_is_tuple = False,\n",
    "            )\n",
    "            self.X_backward = tf.placeholder(tf.float32, (None, None, size))\n",
    "            drop_backward = tf.contrib.rnn.DropoutWrapper(\n",
    "                rnn_cells_backward, output_keep_prob = forget_bias\n",
    "            )\n",
    "            self.hidden_layer_backward = tf.placeholder(\n",
    "                tf.float32, (None, num_layers * size_layer)\n",
    "            )\n",
    "            self.outputs_backward, self.last_state_backward = tf.nn.dynamic_rnn(\n",
    "                drop_backward,\n",
    "                self.X_backward,\n",
    "                initial_state = self.hidden_layer_backward,\n",
    "                dtype = tf.float32,\n",
    "            )\n",
    "\n",
    "        self.outputs = self.outputs_backward - self.outputs_forward\n",
    "        self.Y = tf.placeholder(tf.float32, (None, output_size))\n",
    "        self.logits = tf.layers.dense(self.outputs[-1], output_size)\n",
    "        self.cost = tf.reduce_mean(tf.square(self.Y - self.logits))\n",
    "        self.optimizer = tf.train.AdamOptimizer(learning_rate).minimize(\n",
    "            self.cost\n",
    "        )\n",
    "        \n",
    "def calculate_accuracy(real, predict):\n",
    "    real = np.array(real) + 1\n",
    "    predict = np.array(predict) + 1\n",
    "    percentage = 1 - np.sqrt(np.mean(np.square((real - predict) / real)))\n",
    "    return percentage * 100\n",
    "\n",
    "def anchor(signal, weight):\n",
    "    buffer = []\n",
    "    last = signal[0]\n",
    "    for i in signal:\n",
    "        smoothed_val = last * weight + (1 - weight) * i\n",
    "        buffer.append(smoothed_val)\n",
    "        last = smoothed_val\n",
    "    return buffer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_layers = 1\n",
    "size_layer = 128\n",
    "timestamp = 5\n",
    "epoch = 300\n",
    "dropout_rate = 0.8\n",
    "future_day = test_size\n",
    "learning_rate = 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forecast():\n",
    "    tf.reset_default_graph()\n",
    "    modelnn = Model(\n",
    "        learning_rate, num_layers, df_log.shape[1], size_layer, df_log.shape[1], dropout_rate\n",
    "    )\n",
    "    sess = tf.InteractiveSession()\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    date_ori = pd.to_datetime(df.iloc[:, 0]).tolist()\n",
    "\n",
    "    pbar = tqdm(range(epoch), desc = 'train loop')\n",
    "    for i in pbar:\n",
    "        init_value_forward = np.zeros((1, num_layers * size_layer))\n",
    "        init_value_backward = np.zeros((1, num_layers * size_layer))\n",
    "        total_loss, total_acc = [], []\n",
    "        for k in range(0, df_train.shape[0] - 1, timestamp):\n",
    "            index = min(k + timestamp, df_train.shape[0] - 1)\n",
    "            batch_x_forward = np.expand_dims(\n",
    "                df_train.iloc[k : index, :].values, axis = 0\n",
    "            )\n",
    "            batch_x_backward = np.expand_dims(\n",
    "                np.flip(df_train.iloc[k : index, :].values, axis = 0), axis = 0\n",
    "            )\n",
    "            batch_y = df_train.iloc[k + 1 : index + 1, :].values\n",
    "            logits, last_state_forward, last_state_backward, _, loss = sess.run(\n",
    "                [\n",
    "                    modelnn.logits,\n",
    "                    modelnn.last_state_forward,\n",
    "                    modelnn.last_state_backward,\n",
    "                    modelnn.optimizer,\n",
    "                    modelnn.cost,\n",
    "                ],\n",
    "                feed_dict = {\n",
    "                    modelnn.X_forward: batch_x_forward,\n",
    "                    modelnn.X_backward: batch_x_backward,\n",
    "                    modelnn.Y: batch_y,\n",
    "                    modelnn.hidden_layer_forward: init_value_forward,\n",
    "                    modelnn.hidden_layer_backward: init_value_backward,\n",
    "                },\n",
    "            )\n",
    "            init_value_forward = last_state_forward\n",
    "            init_value_backward = last_state_backward\n",
    "            total_loss.append(loss)\n",
    "            total_acc.append(calculate_accuracy(batch_y[:, 0], logits[:, 0]))\n",
    "        pbar.set_postfix(cost = np.mean(total_loss), acc = np.mean(total_acc))\n",
    "    \n",
    "    future_day = test_size\n",
    "\n",
    "    output_predict = np.zeros((df_train.shape[0] + future_day, df_train.shape[1]))\n",
    "    output_predict[0] = df_train.iloc[0]\n",
    "    upper_b = (df_train.shape[0] // timestamp) * timestamp\n",
    "    init_value_forward = np.zeros((1, num_layers * size_layer))\n",
    "    init_value_backward = np.zeros((1, num_layers * size_layer))\n",
    "\n",
    "    for k in range(0, (df_train.shape[0] // timestamp) * timestamp, timestamp):\n",
    "        batch_x_forward = np.expand_dims(\n",
    "        df_train.iloc[k : k + timestamp, :], axis = 0\n",
    "        )\n",
    "        batch_x_backward = np.expand_dims(\n",
    "            np.flip(df_train.iloc[k : k + timestamp, :].values, axis = 0), axis = 0\n",
    "        )\n",
    "        out_logits, last_state_forward, last_state_backward = sess.run(\n",
    "            [\n",
    "                modelnn.logits,\n",
    "                modelnn.last_state_forward,\n",
    "                modelnn.last_state_backward,\n",
    "            ],\n",
    "            feed_dict = {\n",
    "                modelnn.X_forward: batch_x_forward,\n",
    "                modelnn.X_backward: batch_x_backward,\n",
    "                modelnn.hidden_layer_forward: init_value_forward,\n",
    "                modelnn.hidden_layer_backward: init_value_backward,\n",
    "            },\n",
    "        )\n",
    "        init_value_forward = last_state_forward\n",
    "        init_value_backward = last_state_backward\n",
    "        output_predict[k + 1 : k + timestamp + 1, :] = out_logits\n",
    "\n",
    "    if upper_b != df_train.shape[0]:\n",
    "        batch_x_forward = np.expand_dims(df_train.iloc[upper_b:, :], axis = 0)\n",
    "        batch_x_backward = np.expand_dims(\n",
    "            np.flip(df_train.iloc[upper_b:, :].values, axis = 0), axis = 0\n",
    "        )\n",
    "        out_logits, last_state_forward, last_state_backward = sess.run(\n",
    "            [modelnn.logits, modelnn.last_state_forward, modelnn.last_state_backward],\n",
    "            feed_dict = {\n",
    "                modelnn.X_forward: batch_x_forward,\n",
    "                modelnn.X_backward: batch_x_backward,\n",
    "                modelnn.hidden_layer_forward: init_value_forward,\n",
    "                modelnn.hidden_layer_backward: init_value_backward,\n",
    "            },\n",
    "        )\n",
    "        init_value_forward = last_state_forward\n",
    "        init_value_backward = last_state_backward\n",
    "        output_predict[upper_b + 1 : df_train.shape[0] + 1] = out_logits\n",
    "        future_day -= 1\n",
    "        date_ori.append(date_ori[-1] + timedelta(days = 1))\n",
    "        \n",
    "    init_value_forward = last_state_forward\n",
    "    init_value_backward = last_state_backward\n",
    "    \n",
    "    for i in range(future_day):\n",
    "        o = output_predict[-future_day - timestamp + i:-future_day + i]\n",
    "        o_f = np.flip(o, axis = 0)\n",
    "        out_logits, last_state_forward, last_state_backward = sess.run(\n",
    "            [\n",
    "                modelnn.logits,\n",
    "                modelnn.last_state_forward,\n",
    "                modelnn.last_state_backward,\n",
    "            ],\n",
    "            feed_dict = {\n",
    "                modelnn.X_forward: np.expand_dims(o, axis = 0),\n",
    "                modelnn.X_backward: np.expand_dims(o_f, axis = 0),\n",
    "                modelnn.hidden_layer_forward: init_value_forward,\n",
    "                modelnn.hidden_layer_backward: init_value_backward,\n",
    "            },\n",
    "        )\n",
    "        init_value_forward = last_state_forward\n",
    "        init_value_backward = last_state_backward\n",
    "        output_predict[-future_day + i] = out_logits[-1]\n",
    "        date_ori.append(date_ori[-1] + timedelta(days = 1))\n",
    "    \n",
    "    output_predict = minmax.inverse_transform(output_predict)\n",
    "    deep_future = anchor(output_predict[:, 0], 0.3)\n",
    "    \n",
    "    return deep_future[-test_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W0813 01:22:40.361487 140690772289344 deprecation.py:323] From <ipython-input-6-04b2b1d463f4>:12: BasicRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.SimpleRNNCell, and will be replaced by that in Tensorflow 2.0.\n",
      "W0813 01:22:40.364087 140690772289344 deprecation.py:323] From <ipython-input-6-04b2b1d463f4>:17: MultiRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replaced by that in Tensorflow 2.0.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0813 01:22:40.688215 140690772289344 lazy_loader.py:50] \n",
      "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "  * https://github.com/tensorflow/io (for I/O related ops)\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
      "W0813 01:22:40.691791 140690772289344 deprecation.py:323] From <ipython-input-6-04b2b1d463f4>:30: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
      "W0813 01:22:40.883351 140690772289344 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "W0813 01:22:40.890208 140690772289344 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/rnn_cell_impl.py:459: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
      "W0813 01:22:41.052329 140690772289344 deprecation.py:323] From <ipython-input-6-04b2b1d463f4>:54: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dense instead.\n",
      "train loop: 100%|██████████| 300/300 [01:08<00:00,  4.43it/s, acc=73.8, cost=0.155] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train loop: 100%|██████████| 300/300 [01:09<00:00,  4.32it/s, acc=75, cost=0.151]   \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train loop: 100%|██████████| 300/300 [01:08<00:00,  4.41it/s, acc=75.2, cost=0.14]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train loop: 100%|██████████| 300/300 [01:08<00:00,  4.36it/s, acc=71, cost=0.186]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train loop: 100%|██████████| 300/300 [01:09<00:00,  4.33it/s, acc=84, cost=0.0574]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train loop: 100%|██████████| 300/300 [01:09<00:00,  4.34it/s, acc=72.3, cost=0.166] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train loop: 100%|██████████| 300/300 [01:08<00:00,  4.44it/s, acc=80.4, cost=0.0918]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train loop: 100%|██████████| 300/300 [01:07<00:00,  4.45it/s, acc=79.7, cost=0.101] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train loop: 100%|██████████| 300/300 [01:05<00:00,  4.61it/s, acc=80.6, cost=0.088] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simulation 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "train loop: 100%|██████████| 300/300 [01:08<00:00,  4.40it/s, acc=70.8, cost=0.194] \n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "for i in range(simulation_size):\n",
    "    print('simulation %d'%(i + 1))\n",
    "    results.append(forecast())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracies = [calculate_accuracy(df['Close'].iloc[-test_size:].values, r) for r in results]\n",
    "\n",
    "plt.figure(figsize = (15, 5))\n",
    "for no, r in enumerate(results):\n",
    "    plt.plot(r, label = 'forecast %d'%(no + 1))\n",
    "plt.plot(df['Close'].iloc[-test_size:].values, label = 'true trend', c = 'black')\n",
    "plt.legend()\n",
    "plt.title('average accuracy: %.4f'%(np.mean(accuracies)))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
